from __future__ import  absolute_import
from __future__ import  division
from __future__ import print_function
import argparse
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import  input_data
def main(_):
    print("测试")
    mnist = input_data.read_data_sets("/",one_hot = True)
    x = tf.placeholder(tf.float32,[None,784])
    W = tf.Variable(tf.zeros([784,10]))
    b =  tf.Variable(tf.zeros(10))
    y = tf.matmul(x,W) + b
    y_ = tf.placeholder(tf.float32,[None,10])
    cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y,y_))
    train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
    init = tf.initialize_all_variables()
    sess = tf.Session()
    sess.run(init)
    for i in range(2000):
        batch_xs, batch_ys = mnist.train.next_batch(100)
        sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
        print(i,end=' ')
    correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

if __name__=='__main__':
    tf.app.run(main=main)